aufklarer / CosyVoice3-0.5B-MLX-4bit

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text-to-speech

Introduction of CosyVoice3-0.5B-MLX-4bit

Model Details of CosyVoice3-0.5B-MLX-4bit

CosyVoice3-0.5B MLX 4-bit

CosyVoice 3 text-to-speech model converted to MLX safetensors format with 4-bit quantization for Apple Silicon inference.

Converted from FunAudioLLM/Fun-CosyVoice3-0.5B-2512 .

Model Details
Component Architecture Size
LLM Qwen2.5-0.5B (24L, 896d, 14Q/2KV heads) 467 MB (4-bit)
DiT Flow Matching 22-layer DiT (1024d, 16 heads, 10 ODE steps) 634 MB (fp16)
HiFi-GAN Vocoder NSF + F0 predictor + ISTFT 79 MB (fp16)
Total ~1.2 GB
Pipeline
Text → LLM (Qwen2.5-0.5B) → Speech Tokens (FSQ 6561) → DiT Flow Matching → Mel (80-band) → HiFi-GAN → Audio (24kHz)
Languages

Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian

Files
  • llm.safetensors — LLM weights (4-bit quantized)
  • flow.safetensors — DiT flow matching decoder (fp16)
  • hifigan.safetensors — HiFi-GAN vocoder (fp16, weight-norm folded)
  • config.json — Model configuration
Conversion Details
  • LLM: 4-bit quantization (group_size=64) of attention projections, MLP, and speech head
  • Flow: fp16 (flow matching is sensitive to quantization)
  • HiFi-GAN: fp16 with weight normalization folded ( w = g * v / ||v|| )
  • Conv1d weights transposed from PyTorch [out, in, kernel] to MLX [out, kernel, in]
Usage

For use with Speech Swift :

import CosyVoiceTTS

let model = try await CosyVoiceTTSModel.fromPretrained()
let audio = model.synthesize(text: "Hello, how are you?", language: "english")
License

Apache 2.0 (same as upstream CosyVoice 3)

Citation
@article{du2025cosyvoice3,
  title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training},
  author={Du, Zhihao and others},
  journal={arXiv preprint arXiv:2505.17589},
  year={2025}
}

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